memnet (version 0.1.0)

community_graph: Create community graph

Description

Create a graph from verbal fluency data by adding edges for words that occur within a window size l and retaining those that occur more frequently than min_cooc and the expectations number of chance productions co- occurences based on 1-crit.

Usage

community_graph(dat, l = 3L, min_cooc = 2L, crit = 0.05)

Arguments

dat

list of character vectors containing the fluency productions.

l

an integer specifying the window size. The internal upper limit of l is the number of productions.

min_cooc

integer specifying the minimum number of times two words have to coocur within a window size of l to consider including an edge between them.

crit

a numeric within [0,1] specifiying the type-1 error rate of including an edge between unconnected words.

Value

A matrix

References

Goni, J., Arrondo, G., Sepulcre, J., Martincorena, I., de Mendiz<U+00E1>bal, N. V., Corominas-Murtra, B., ... & Villoslada, P. (2011). The semantic organization of the animal category: evidence from semantic verbal fluency and network theory. Cognitive processing, 12(2), 183-196.

Wulff, D. U., Hills, T., & Mata, R. (2018, October 29). Structural differences in the semantic networks of younger and older adults. https://doi.org/10.31234/osf.io/s73dp

Examples

# NOT RUN {
# get animal fluency data
data(animal_fluency)

# infer influence network
inferred_network = community_graph(animal_fluency)

# Simulate -----

# generate watts strogatz graph
network = grow_ws(n = 200, k = 10, p = .5)

# generate fluency data
# sets string equal TRUE as community_graph expects mode character
fluency_data = fluency(get_adjlist(network), rep(10, 100), string = TRUE)

# infer fluency network
inferred_network = community_graph(fluency_data)

# }